The use of lattice structures has received increasing interest in various engineering applications owing to their high strength to weight ratio. Advances in additive manufacturing technologies enabled the manufacturing of highly complex lattice structures such as triply periodic minimal surface (TPMS) models in recent years. The application of simulation tools is expected to enhance the performance of these designs further. Therefore, it is vital to understand their accuracy and computational efficiency. In this paper, modal characterization of additively manufactured TPMS structures is studied using five different modeling methods for a beam, which is composed of primitive, diamond, IWP, and gyroid unit cells. These methods include (1) shell modeling, (2) solid modeling, (3) homogenization, (4) super-element modeling, and (5) voxelization. The modal characterization is performed by using modal analysis, and the aforementioned models are compared in terms of their computational efficiency and accuracy. The results are experimentally validated by performing an experimental modal testing on a test specimen, made of HS188, and manufactured by direct metal laser melting. Finally, the relationship between the modal characteristics and volume fraction is derived by carrying out a parametric study for all types of TMPS structures considered in this paper. The complex modal characteristics of different TPMS types suggest that they can be jointly used to meet the ever-challenging design requirements using the modeling guidelines proposed in this study.
Dynamic models of physical systems with physically meaningful states and parameters have become increasingly important, for design, control and even procurement decisions. The successful use of models in these contexts requires that the models be of sufficient quality. However, while algorithms have been developed to help formulate and integrate physical system models, as well as to generate minimum complexity physical system models, algorithms to assess the “quality” of dynamic system models have not been produced. This is true even if the attributes of model are limited to accuracy and validity. The objective of this paper is to introduce a new methodology that systematically quantifies the accuracy of a predicted system response and determines the validity of the physical system model used to predict the system response. The accuracy and validity of the model are evaluated using statistical properties of measured system response. The new algorithm is called Accuracy & Validation Algorithm for Simulation (AVASIM), and is a time-domain perspective comparing the model’s time trajectories at user-defined points of interest as well as over the entire simulation horizon. To illustrate AVASIM, the quality of a handling model of a DaimlerChrysler Grand Cherokee is compared to the measurements obtained from that vehicle subjected to known steering inputs. Results demonstrate that the accuracy and validity of the Grand Cherokee model can be systematically assessed using the proposed methodology, and, thus, AVASIM appears to be a powerful tool for assessing the quality of system models.
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